Apple
Machine Learning Compute Efficiency Lead, Infrastructure & Planning
$185K–$325K
Cupertino, California · Full-time · Posted Apr 23, 2026
- Experience
- 7+ years in ML infrastructure, systems architecture, or efficiency/optimization roles at scale.
- Education
- BS in Computer Science, Computer Engineering, or equivalent practical experience; MS or PhD preferred.
About the job
Leads compute efficiency and infrastructure strategy for Apple’s large-scale ML inference workloads, improving utilization, performance, capacity allocation, and serving costs.
What you'll do
- Manage compute for inference workloads across GPUs, TPUs, and custom silicon; develop resource strategies with ML engineering teams.
- Optimize ML workloads through performance improvements, utilization gains, cost reduction, and root-cause analysis.
- Architect capacity allocation and workload scheduling solutions, and represent engineering needs to internal and cloud infrastructure providers.
What they ask for
- BS in Computer Science, Computer Engineering, or equivalent practical experience.
- 7+ years in ML infrastructure, systems architecture, or efficiency/optimization roles at scale.
- Strong understanding of foundation model inference/serving, distributed training, GPU/TPU utilization, memory hierarchies, and cluster scheduling.
- Demonstrated ability to drive cross-organizational technical initiatives, analyze capacity or efficiency metrics, and communicate with technical and executive audiences.
Nice to have
- MS or PhD in a relevant field.
- Experience with foundation-model serving, PyTorch, JAX, Slurm, Kubernetes, GPU/TPU hardware, FinOps, capacity planning, or TCO modeling.
Pay
$184,700 and $324,800 USD base salary
Tools
GPU, TPU, custom Apple Silicon, PyTorch, JAX, Slurm, Kubernetes
AI focus
Improves the efficiency, scalability, and cost of large-scale foundation-model inference and serving; supports ML workloads and resource planning.
Model-written summary · the original posting is authoritative